WEBDec 1, 2014 · The experimental result shows that feature vectors constructed by the dimensionless parameters input support vector machine can automatically identify the CoalRock interface. In order to identify the CoalRock interface of mechanized caving mining, a new method based on dimensionless parameters and support vector machine .
WEBApr 5, 2022 · In this section, we discuss several typical coal classifiion methods. The use of machine learning methods in combination with spectroscopy to classify coal is based mainly on ELM, random forest (RF) and support vector machine (SVM) [38], [39]. The comparison results are presented in Table 2. The proposed method outperforms these .
WEBFeb 20, 2023 · Computervisionbased separation methods for coal gangue face challenges due to the harsh environmental conditions in the mines, leading to the reduction of separation accuracy. So, rather than purely depending on the image features to distinguish the coal gangue, it is meaningful to utilize fixed coal characteristics like .
WEBSep 1, 2021 · The workflow combines physicsbased simulation, laboratory experiments, and a datadriven machine learning approach for estimating the permeability profile. As part of this workflow, several coal specimens from the study coal seam are first tested under different stresses to measure their permeability, density, and ultrasonic responses.
WEBBecause of its complex working environment, most coal mines take belt conveyor as the main transportation equipment. However, in the process of transportation, due to longtime and highintensity operation, the belt is very easy to be damaged by gangue, iron and other foreign matters doped in coal, resulting in unnecessary losses. Foreign objects in the .
WEBJun 1, 2023 · Feng et al. (2015) proved that a support vector machine (SVM) could perform well in terms of accuracy to predict the gross calorific value (GCV) ... In this study, the GBRT model was used to predict the HHV of coal based on the proximate analysis data, and the model adopted optimal parameters selected through crossvalidation. ...
WEBAbstract. Read online. The classifiion of surrounding rock stability of coal roadway has important theoretical and practical significance for the design, construction and management of onsite rock mass paper selected seven key indexes that affect the surrounding rock stability of coal roadway, collected the samples through field .
WEBFeb 1, 2024 · Coal structure identifiion based on PSOSVM. In this study, the coal structure prediction model was established based on 175 sets of data (53 undeformed coal, 67 aclastic coal and 54 granulated coal) from 20 wells, excluding 10 sets of data from the No. 3 coal seam in Well M19 (4 undeformed coal, 1 aclastic coal and 2 .
WEBJan 1, 2013 · Maixi Lu, Zhou C (2009) Coal calorific value prediction with linear regression and artificial neural network. Coal Sci Technol 37:117–120. Google Scholar Jiang W, Hongqi W, Qu T (2011) Prediction of the calorific value for coal based on the SVM with parameters optimized by genetic algorithm. Thermal Power Gener 40:14–19
WEBMay 4, 2023 · Spontaneous combustion of coal leading to mine fire is a major problem in most of the coal mining countries in the world. It causes major loss to the Indian economy. The liability of coal to spontaneous combustion varies from place to place and mainly depends on the coal intrinsic properties and other geomining factors. Hence, the .
WEBKeeping in mind the various problems related to gas leakage causing accidents in the coal mine, this paper depicts coal monitoring system using wireless sensor networks and IoT, which can monitor the various gas and temperature parameters and take action with the help of multimodal logistic regression algorithm applied on the real time collected data .
WEBJan 13, 2022 · Since hundreds or thousands of patches can be extracted from each image, the patch database is much larger than the rock and coal image database. The machine learning process is based on the patches. As discussed earlier, the RGB images are stored as threedimensional arrays, and the extraction of patches is accomplished by extracting .
WEBJun 1, 2019 · Wang et al. [9], [10] proposed a coal component analysis model based on a support vector machine, a partial least squares regression algorithm and nearinfrared reflectance spectroscopy. The model analyzed six components of coal, including total moisture, inherent moisture, ash, volatile matter, fixed carbon, and sulfur.
WEBNov 1, 2021 · In this study, we developed an automatic Ppick quality control model based on machine learning to identify useable/unusable Ppicks. We used five waveform parameters, including signaltonoise ratio (SNR), signaltonoise variance ratio (SNVR), Pphase startingup slope ( K p ), shorttime zerocrossing rate (ZCR) and peak amplitude .
WEBDOI: / Corpus ID: ; The appliion of machine learning models based on particles characteristics during coal slime flotation article{Zhao2021TheAO, title={The appliion of machine learning models based on particles characteristics during coal slime flotation}, author={Binglong Zhao and .
WEBSpontaneous combustion of coal leading to mine fire is a major problem in most of the coal mining countries in the world. It causes major loss to the Indian economy. The liability of coal to spontaneous combustion varies from place to place and mainly depends on the coal intrinsic properties and oth .
WEBApr 12, 2022 · Machine learning prediction of calorific value of coal based on the hybrid analysis. April 2022. International Journal of Coal Preparation and Utilization 43 (1):122. DOI: / ...
WEBOct 22, 2021 · Appliion of Volume Detection Based on Machine Vision in Coal and Gangue Separation. October 2021. DOI: / Conference: 2021 IEEE 5th Conference on Energy Internet and ...
WEBJun 3, 2021 · This paper uses this as a starting point to propose a distributed support vector machine model based on a cloud computing platform. The model is based on the existing popular MapReduce distributed computing framework, and completes the classifiion and prediction work in the coal system in a distributed manner. ... Environmental cost control ...
WEBJul 1, 2022 · Abstract. In this paper, YOLOv4 algorithm based on deep learning is used to detect coal gangue. Firstly, the data set of coal gangue was made, which provides sufficient data for the training and verifiion of the detection algorithm model. Then, the coal gangue data set was used to test the influence of the combined use of optimization ...
WEBMay 1, 2013 · A neural network prediction method based on an improved SMOTE algorithm expanding a small sample dataset and optimizing a deep confidence network was proposed, which can be used to better predict and analyze coal mine water inrush accidents, improve the accuracy of water in rush accident prediction, and encourage the .
WEBMine work face gas emission quantity is an important mine design basis, which also has important practical significance for guide mine design, ventilation and safety production. Mine gas emission quantity and work face multi factors have complex nonlinear relationship. The paper built the work face gas emission prediction support vector .
WEBSep 1, 2023 · Effects of Nibased composite coatings on failure mechanism and wear resistance of cutting picks on coal shearer machine. ... After completing the field studies in a real scale coal cutting machine and measuring the wear rate of the coated and uncoated picks refer to cutting operation length, the results of these measurements were analyzed .
WEBTherefore, this manuscript proposes a new identifiion method of surface cracks from UAV images based on machine learning in coal mining areas. First, the acquired UAV image is cut into small subimages, and divided into four datasets according to the characteristics of background information: Bright Ground, Dark Dround, Withered .
WEBJan 4, 2024 · Cocombustion of coal and biomass has the potential to reduce the cost of power generation in plants. However, because of the high content of the alkali metal of biomass ash, cocombustion of these two fuels leads to unpredictable ash fusion temperature (AFT). This study conducted experiments to measure the AFT of straw, .
WEBDec 23, 2022 · failure of coal, coal bursting liability (CBL) is the basis of the research on the early warning and prevention of coal burst. T o accurately classify the CBL level, the supportvectormachine (SVM)
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